Papers Policy Gradient Methods
“Policy Gradient Methods” 태그가 달린 논문 382편 · 필터 해제
Competitive Policy Optimization
A core challenge in policy optimization in competitive Markov decision processes is the design of efficient optimization methods with desirable convergence and stability properties. To tackle this, we propose competitive…
Policy Gradient MethodsLifelong Learning of Factored Policies via Policy Gradients
Policy gradient methods have shown success in learning continuous control policies for high-dimensional dynamical systems. A major downside of such methods is the amount of exploration they require before yielding high-p…
continuous-controlContinuous ControlLifelong learningPolicy Gradient MethodsZeroth-Order Supervised Policy Improvement
Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL). However, PG algorithms rely on exploiting the value function being learned with the first-order update locally, which results in limit…
continuous-controlContinuous ControlPolicy Gradient MethodsQ-Learning+1Jointly Learning Environments and Control Policies with Projected Stochastic Gradient Ascent
We consider the joint design and control of discrete-time stochastic dynamical systems over a finite time horizon. We formulate the problem as a multi-step optimization problem under uncertainty seeking to identify a sys…
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning (RL)Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learning
A fundamental challenge in reinforcement learning is to learn policies that generalize beyond the operating domains experienced during training. In this paper, we approach this challenge through the following invariance …
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)On the Global Convergence Rates of Softmax Policy Gradient Methods
We make three contributions toward better understanding policy gradient methods in the tabular setting. First, we show that with the true gradient, policy gradient with a softmax parametrization converges at a $O(1/t)$ r…
Open-Ended Question AnsweringPolicy Gradient MethodsImproving Sample Efficiency and Multi-Agent Communication in RL-based Train Rescheduling
We present preliminary results from our sixth placed entry to the Flatland international competition for train rescheduling, including two improvements for optimized reinforcement learning (RL) training efficiency, and t…
Policy Gradient Methodsreinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement Learning via Projection on a Safe Set: How to Achieve Optimality?
For all its successes, Reinforcement Learning (RL) still struggles to deliver formal guarantees on the closed-loop behavior of the learned policy. Among other things, guaranteeing the safety of RL with respect to safety-…
Policy Gradient MethodsQ-Learningreinforcement-learningReinforcement Learning+2Exchangeable Input Representations for Reinforcement Learning
Poor sample efficiency is a major limitation of deep reinforcement learning in many domains. This work presents an attention-based method to project neural network inputs into an efficient representation space that is in…
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1Stochastic Recursive Momentum for Policy Gradient Methods
In this paper, we propose a novel algorithm named STOchastic Recursive Momentum for Policy Gradient (STORM-PG), which operates a SARAH-type stochastic recursive variance-reduced policy gradient in an exponential moving a…
Policy Gradient MethodsPolicy-Aware Model Learning for Policy Gradient Methods
This paper considers the problem of learning a model in model-based reinforcement learning (MBRL). We examine how the planning module of an MBRL algorithm uses the model, and propose that the model learning module should…
modelModel-based Reinforcement LearningPolicy Gradient MethodsReinforcement LearningGACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction
In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider multiple modes, and are not able to adapt t…
Policy Gradient MethodsReinforcement LearningOn the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning
We consider Model-Agnostic Meta-Learning (MAML) methods for Reinforcement Learning (RL) problems, where the goal is to find a policy using data from several tasks represented by Markov Decision Processes (MDPs) that can …
Meta-LearningMeta Reinforcement LearningPolicy Gradient Methodsreinforcement-learning+2Statistically Efficient Off-Policy Policy Gradients
Policy gradient methods in reinforcement learning update policy parameters by taking steps in the direction of an estimated gradient of policy value. In this paper, we consider the statistically efficient estimation of p…
Policy Gradient MethodsReinforcement LearningBayesian Residual Policy Optimization: Scalable Bayesian Reinforcement Learning with Clairvoyant Experts
Informed and robust decision making in the face of uncertainty is critical for robots that perform physical tasks alongside people. We formulate this as Bayesian Reinforcement Learning over latent Markov Decision Process…
Decision MakingPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks
Progress in multiagent intelligence research is fundamentally limited by the number and quality of environments available for study. In recent years, simulated games have become a dominant research platform within reinfo…
Policy Gradient MethodsReinforcement LearningDeep Reinforcement Learning based Blind mmWave MIMO Beam Alignment
Directional beamforming is a crucial component for realizing robust wireless communication systems using millimeter wave (mmWave) technology. Beam alignment using brute-force search of the space introduces time overhead …
Deep Reinforcement LearningPolicy Gradient Methodsreinforcement-learningReinforcement Learning+1A Nonparametric Off-Policy Policy Gradient
Reinforcement learning (RL) algorithms still suffer from high sample complexity despite outstanding recent successes. The need for intensive interactions with the environment is especially observed in many widely popular…
Density EstimationPolicy Gradient MethodsReinforcement LearningReinforcement Learning (RL)Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods
The policy gradient theorem is defined based on an objective with respect to the initial distribution over states. In the discounted case, this results in policies that are optimal for one distribution over initial state…
Policy Gradient MethodsFast Efficient Hyperparameter Tuning for Policy Gradient Methods
The performance of policy gradient methods is sensitive to hyperparameter settings that must be tuned for any new application. Widely used grid search methods for tuning hyperparameters are sample inefficient and computa…
Policy Gradient Methods